Servers host the data segments and serve queries off the data they host. There are two types of servers:

Offline Offline servers are responsible for downloading segments from the segment store, to host and serve queries off. When a new segment is uploaded to the controller, the controller decides the servers (as many as replication) that will host the new segment and notifies them to download the segment from the segment store. On receiving this notification, the servers download the segment file and load the segment onto the server, to server queries off them.

Real-time Real-time servers directly ingest from a real-time stream (such as Kafka or EventHubs). Periodically, they make segments of the in-memory ingested data, based on certain thresholds. This segment is then persisted onto the segment store.

Pinot servers are modeled as Helix participants, hosting Pinot tables (referred to as resources in Helix terminology). Segments of a table are modeled as Helix partitions (of a resource). Thus, a Pinot server hosts one or more Helix partitions of one or more helix resources (i.e. one or more segments of one or more tables).

Starting a server

Make sure you've set up Zookeeper. If you're using Docker, make sure to pull the Pinot Docker image. To start a server:

Usage: StartServer
	-serverHost               <String>                      : Host name for controller. (required=false)
	-serverPort               <int>                         : Port number to start the server at. (required=false)
	-serverAdminPort          <int>                         : Port number to serve the server admin API at. (required=false)
	-dataDir                  <string>                      : Path to directory containing data. (required=false)
	-segmentDir               <string>                      : Path to directory containing segments. (required=false)
	-zkAddress                <http>                        : Http address of Zookeeper. (required=false)
	-clusterName              <String>                      : Pinot cluster name. (required=false)
	-configFileName           <Config File Name>            : Broker Starter Config file. (required=false)
	-help                                                   : Print this message. (required=false)

docker run \
    --network=pinot-demo \
    --name pinot-server \
    -d ${PINOT_IMAGE} StartServer \
    -zkAddress pinot-zookeeper:2181

bin/pinot-admin.sh StartServer \
    -zkAddress localhost:2181

Brokers handle Pinot queries. They accept queries from clients and forward them to the right servers. They collect results back from the servers and consolidate them into a single response, to send back to the client.

Pinot brokers are modeled as Helix spectators. They need to know the location of each segment of a table (and each replica of the segments) and route requests to the appropriate server that hosts the segments of the table being queried.

The broker ensures that all the rows of the table are queried exactly once so as to return correct, consistent results for a query. The brokers may optimize to prune some of the segments as long as accuracy is not sacrificed.

Helix provides the framework by which spectators can learn the location in which each partition of a resource (i.e. participant) resides. The brokers use this mechanism to learn the servers that host specific segments of a table.

In the case of hybrid tables, the brokers ensure that the overlap between real-time and offline segment data is queried exactly once, by performing offline and real-time federation.

Let's take this example, we have real-time data for 5 days - March 23 to March 27, and offline data has been pushed until Mar 25, which is 2 days behind real-time. The brokers maintain this time boundary.

Suppose, we get a query to this table : select sum(metric) from table. The broker will split the query into 2 queries based on this time boundary – one for offline and one for real-time. This query becomes select sum(metric) from table_REALTIME where date >= Mar 25 and select sum(metric) from table_OFFLINE where date < Mar 25

The broker merges results from both these queries before returning the result to the client.

Starting a broker

Make sure you've set up Zookeeper. If you're using Docker, make sure to pull the Pinot Docker image. To start a broker:

docker run \
    --network=pinot-demo \
    --name pinot-broker \
    -d ${PINOT_IMAGE} StartBroker \
    -zkAddress pinot-zookeeper:2181

bin/pinot-admin.sh StartBroker \
  -zkAddress localhost:2181 \
  -clusterName PinotCluster \
  -brokerPort 7000

A cluster is a set of nodes comprising of servers, brokers, controllers and minions.

Pinot uses for cluster management. Helix is a cluster management framework that manages replicated, partitioned resources in a distributed system. Helix uses Zookeeper to store cluster state and metadata.

Cluster configuration

Cluster components

Helix divides nodes into logical components based on their responsibilities:

Participant

Participants are the nodes that host distributed, partitioned resources

Spectator

Spectators are the nodes that observe the current state of each participant and use that information to access the resources. Spectators are notified of state changes in the cluster (state of a participant, or that of a partition in a participant).

Controller

The node that observes and controls the Participant nodes. It is responsible for coordinating all transitions in the cluster and ensuring that state constraints are satisfied while maintaining cluster stability.

Logical view

Another way to visualize the cluster is a logical view, where:

Set up a Pinot cluster

To set up a cluster, see one of the following guides:

A minion is a standby component that leverages the to offload computationally intensive tasks from other components.

It can be attached to an existing Pinot cluster and then execute tasks as provided by the controller. Custom tasks can be plugged via annotations into the cluster. Some typical minion tasks are:

• Segment creation

• Segment purge

• Segment merge

Starting a Minion

Make sure you've . If you're using Docker, make sure to . To start a minion:

Interfaces

PinotTaskGenerator

PinotTaskGenerator interface defines the APIs for the controller to generate tasks for minions to execute.

PinotTaskExecutorFactory

Factory for PinotTaskExecutor which defines the APIs for Minion to execute the tasks.

MinionEventObserverFactory

Factory for MinionEventObserver which defines the APIs for task event callbacks on minion.

Built-in tasks

SegmentGenerationAndPushTask

The PushTask can fetch files from an input folder e.g. from a S3 bucket and converts them into segments. The PushTask converts one file into one segment and keeps file name in segment metadata to avoid duplicate ingestion. Below is an example task config to put in TableConfig to enable this task. The task is scheduled every 10min to keep ingesting remaining files, with 10 parallel task at max and 1 file per task.

NOTE: You may want to simply omit "tableMaxNumTasks" due to this caveat: the task generates one segment per file, and derives segment name based on the time column of the file. If two files happen to have same time range and are ingested by tasks from different schedules, there might be segment name conflict. To overcome this issue for now, you can omit “tableMaxNumTasks” and by default it’s Integer.MAX_VALUE, meaning to schedule as many tasks as possible to ingest all input files in a single batch. Within one batch, a sequence number suffix is used to ensure no segment name conflict. Because the sequence number suffix is scoped within one batch, tasks from different batches might encounter segment name conflict issue said above.

RealtimeToOfflineSegmentsTask

MergeRollupTask

Enable tasks

Tasks are enabled on a per-table basis. To enable a certain task type (e.g. myTask) on a table, update the table config to include the task type:

Under each enable task type, custom properties can be configured for the task type.

There are also two task configs to be set as part of cluster configs like below. One controls task's overall timeout (1hr by default) and one for how many tasks to run on a single minion worker (1 by default).

Schedule tasks

Auto-schedule

There are 2 ways to enable task scheduling:

Controller level schedule for all minion tasks

Tasks can be scheduled periodically for all task types on all enabled tables. Enable auto task scheduling by configuring the schedule frequency in the controller config with the key controller.task.frequencyPeriod. This takes period strings as values, e.g. 2h, 30m, 1d.

Per table and task level schedule

Tasks can also be scheduled based on cron expressions. The cron expression is set in the schedule config for each task type separately. This config in the controller config, controller.task.scheduler.enabled should be set to true to enable cron scheduling.

Manual schedule

Tasks can be manually scheduled using the following controller rest APIs:

Plug-in custom tasks

To plug in a custom task, implement PinotTaskGenerator, PinotTaskExecutorFactory and MinionEventObserverFactory (optional) for the task type (all of them should return the same string for getTaskType()), and annotate them with the following annotations:

After annotating the classes, put them under the package of name org.apache.pinot.*.plugin.minion.tasks.*, then they will be auto-registered by the controller and minion.

Example

Task Manager UI

In the Pinot UI, there is Minion Task Manager tab under Cluster Manager page. From that minion task manager tab, one can find a lot of task related info for troubleshooting. Those info are mainly collected from the Pinot controller that schedules tasks or Helix that tracks task runtime status. There are also buttons to schedule tasks in an ad hoc way. Below are some brief introductions to some pages under the minion task manager tab.

This one shows which types of Minion Task have been used. Essentially which task types have created their task queues in Helix.

Clicking into a task type, one can see the tables using that task. And a few buttons to stop the task queue, cleaning up ended tasks etc.

Then clicking into any table in this list, one can see how the task is configured for that table. And the task metadata if there is one in ZK. For example, MergeRollupTask tracks a watermark in ZK. If the task is cron scheduled, the current and next schedules are also shown in this page like below.

At the bottom of this page is a list of tasks generated for this table for this specific task type. Like here, one MergeRollup task has been generated and completed.

Clicking into a task from that list, we can see start/end time for it, and the sub tasks generated for that task (as context, one minion task can have multiple sub-tasks to process data in parallel). In this example, it happened to have one sub-task here, and it shows when it starts and stops and which minion worker it's running.

Clicking into this subtask, one can see more details about it like the input task configs and error info if the task failed.

Task-related metrics

There is a controller job that runs every 5 minutes by default and emits metrics about Minion tasks scheduled in Pinot. The following metrics are emitted for each task type:

• NumMinionTasksInProgress: Number of running tasks

• NumMinionSubtasksRunning: Number of running sub-tasks

• NumMinionSubtasksWaiting: Number of waiting sub-tasks (unassigned to a minion as yet)

• NumMinionSubtasksError: Number of error sub-tasks (completed with an error/exception)

• PercentMinionSubtasksInQueue: Percent of sub-tasks in waiting or running states

• PercentMinionSubtasksInError: Percent of sub-tasks in error

The controller also emits metrics about how tasks are cron scheduled:

• cronSchedulerJobScheduled: Number of current cron schedules registered to be triggered regularly according their cron expressions. It's a Gauge.

• cronSchedulerJobTrigger: Number of cron scheduled triggered, as a Meter.

• cronSchedulerJobSkipped: Number of late cron scheduled skipped, as a Meter.

• cronSchedulerJobExecutionTimeMs: Time used to complete task generation, as a Timer.

For each task, the minion will emit these metrics:

• TASK_QUEUEING: Task queueing time (task_dequeue_time - task_inqueue_time), assuming the time drift between helix controller and pinot minion is minor, otherwise the value may be negative

• TASK_EXECUTION: Task execution time, which is the time spent on executing the task

• NUMBER_OF_TASKS: number of tasks in progress on that minion. Whenever a Minion starts a task, increase the Gauge by 1, whenever a Minion completes (either succeeded or failed) a task, decrease it by 1

• NUMBER_TASKS_EXECUTED: Number of tasks executed, as a Meter.

• NUMBER_TASKS_COMPLETED: Number of tasks completed, as a Meter.

• NUMBER_TASKS_CANCELLED: Number of tasks cancelled, as a Meter.

• NUMBER_TASKS_FAILED: Number of tasks failed, as a Meter. Different from fatal failure, the task encountered an error which can not be recovered from this run, but it may still succeed by retrying the task.

• NUMBER_TASKS_FATAL_FAILED: Number of tasks fatal failed, as a Meter. Different from failure, the task encountered an error, which will not be recoverable even with retrying the task.

A tenant is a logical component defined as a group of server/broker nodes with the same Helix tag.

In order to support multi-tenancy, Pinot has first-class support for tenants. Every table is associated with a server tenant and a broker tenant. This controls the nodes that will be used by this table as servers and brokers. This allows all tables belonging to a particular use case to be grouped under a single tenant name.

The concept of tenants is very important when the multiple use cases are using Pinot and there is a need to provide quotas or some sort of isolation across tenants. For example, consider we have two tables Table A and Table B in the same Pinot cluster.

We can configure Table A with server tenant Tenant A and Table B with server tenant Tenant B. We can tag some of the server nodes for Tenant A and some for Tenant B. This will ensure that segments of Table A only reside on servers tagged with Tenant A, and segment of Table B only reside on servers tagged with Tenant B. The same isolation can be achieved at the broker level, by configuring broker tenants to the tables.

No need to create separate clusters for every table or use case!

Tenant configuration

This section contains two main fields broker and server , which decide the tenants used for the broker and server components of this table.

In the above example:

• The table will be served by brokers that have been tagged as brokerTenantName_BROKER in Helix.

• If this were an offline table, the offline segments for the table will be hosted in Pinot servers tagged in Helix as serverTenantName_OFFLINE

• If this were a real-time table, the real-time segments (both consuming as well as completed ones) will be hosted in pinot servers tagged in Helix as serverTenantName_REALTIME.

Create a tenant

Broker tenant

Here's a sample broker tenant config. This will create a broker tenant sampleBrokerTenant by tagging three untagged broker nodes as sampleBrokerTenant_BROKER.

To create this tenant use the following command. The creation will fail if number of untagged broker nodes is less than numberOfInstances.

Server tenant

Here's a sample server tenant config. This will create a server tenant sampleServerTenant by tagging 1 untagged server node as sampleServerTenant_OFFLINE and 1 untagged server node as sampleServerTenant_REALTIME.

To create this tenant use the following command. The creation will fail if number of untagged server nodes is less than offlineInstances + realtimeInstances.

The Pinot controller is responsible for the following:

• Maintaining global metadata (e.g., configs and schemas) of the system with the help of Zookeeper which is used as the persistent metadata store.

• Hosting the Helix Controller and managing other Pinot components (brokers, servers, minions)

• Maintaining the mapping of which servers are responsible for which segments. This mapping is used by the servers to download the portion of the segments that they are responsible for. This mapping is also used by the broker to decide which servers to route the queries to.

• Serving admin endpoints for viewing, creating, updating, and deleting configs, which are used to manage and operate the cluster.

• Serving endpoints for segment uploads, which are used in offline data pushes. They are responsible for initializing real-time consumption and coordination of persisting real-time segments into the segment store periodically.

• Undertaking other management activities such as managing retention of segments, validations.

For redundancy, there can be multiple instances of Pinot controllers. Pinot expects that all controllers are configured with the same back-end storage system so that they have a common view of the segments (e.g. NFS). Pinot can use other storage systems such as HDFS or .

Running the periodic task manually

The controller runs several periodic tasks in the background, to perform activities such as management and validation. Each periodic task has to define the run frequency and default frequency. Each task runs at its own schedule or can also be triggered manually if needed. The task runs on the lead controller for each table.

For period task configuration details, see .

Use the GET /periodictask/names API to fetch the names of all the periodic tasks running on your Pinot cluster.

To manually run a named periodic task, use the GET /periodictask/run API:

The Log Request Id (api-09630c07) can be used to search through pinot-controller log file to see log entries related to execution of the Periodic task that was manually run.

If tableName (and its type OFFLINE or REALTIME) is not provided, the task will run against all tables.

Starting a controller